eBook Simulation-Based Algorithms for Markov Decision Processes, 2nd Edition

  • Published By:
  • ISBN-10: 1447150228
  • ISBN-13: 9781447150220
  • DDC: 658.4033
  • Grade Level Range: College Freshman - College Senior
  • 229 Pages | eBook
  • Original Copyright 2013 | Published/Released May 2014
  • This publication's content originally published in print form: 2013
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About

Overview

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.  Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable.  In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function.  Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.

Table of Contents

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
Dedication.
Preface to the 2nd Edition.
Contents.
Selected Notation and Abbreviations.
1: Markov Decision Processes.
2: Multi-stage Adaptive Sampling Algorithms.
3: Population-Based Evolutionary Approaches.
4: Model Reference Adaptive Search.
5: On-Line Control Methods via Simulation.
References.
Index.